Presentation on theme: "SenseSwarm: A Perimeter-based Data Acquisition Framework for Mobile Sensor Networks Demetrios Zeinalipour-Yazti (Open Univ. of Cyprus) Panayiotis Andreou."— Presentation transcript:
2 Mobile Sensors Artifacts created by the distributed robotics and low power embedded systems areas. Characteristics Large-scale, highly distributed and energy- sensitive, as their stationary counterparts. Feature explicit (e.g., motor) or implicit (sea/air current) mechanisms which enable movement. CotsBots (UC-Berkeley) MilliBots (CMU) LittleHelis (USC) SensorFlock (U of Colorado Boulder)
3 Mobile Sensor Networks (MSNs) What is a Mobile Sensor Network? A new class of networks where small sensing devices move in space over time. –Generate spatio-temporal records (x,y,t,other) Advantages Controlled Mobility –Can recover network connectivity. –Can eliminate expensive overlay links. Focused Sampling –Change sampling rate based on spatial location (i.e., move closer to the physical phenomenon).
4 Applications of MSNs Chemical Dispersion Sampling Identify the existence of toxic plumes. Graphic courtesy of: J. Allred et al. "SensorFlock: An Airborne Wireless Sensor Network of Micro-Air Vehicles", In ACM SenSys 2007. Micro Air VehiclesGround Station
5 A Futuristic Application of MSNs Mars Exploration: Find water on the red planet. Solution: Mobile Sensor Networks Potentially Cheaper More Fault Tolerant MARS WATER X X Queries Query 1: Has the MSN identified any water? Query 2: Where exactly? Failures SINK
6 Our Data/Querying Model Queries are historic (the sink is usually OFF) –Thus, results have to be stored in-network. Sensor failures might happen frequently. –Thus, replication techniques are adopted New events are more likely on the perimeter –e.g., the toxic plume example, identify oil-spills in oceans, etc., … –Thus, schedule acquisition on the perimeter MARS SINK
7 Our Solution Outline SenseSwarm: A new framework where data acquisition is scheduled at perimeter sensors and storage at core nodes. s1 s2 s3 s4 s5 s6 s7 s8 Swarm (or Flock): a group of objects that exhibit a polarized, non-colliding and aggregate motion.
8 Presentation Outline Motivation – Definitions The SenseSwarm Framework Task 1: Perimeter Construction Task 2: Data Acquisition Task 3: Data Replication Task 4: Query Execution Experimentation Conclusions & Future Work
9 Task 1: Perimeter Construction Problem: How do we construct the perimeter for N sensors? Centralized Perimeter Algorithm (CPA) Collect all sensor coordinates Calculate Perimeter Disseminate Perimeter Disadvantage: Collecting all coordinates requires the transfer of O(N 2 ) (x,y)-pairs – too expensive!
10 Task 1: Perimeter Construction Our approach: Construct the perimeter in a distributed manner. Our Algorithm: Perimeter Algorithm (PA) Find the sensor with the minimum y coordinate using TAG (denoted as s min ). Inform s min about this choice. s min initiates the recursive perimeter construction step using counterclockwise turns. Right Left s1 smin s3
11 Task 1: Perimeter Construction s1 s2 s3 s4 s5 s6 s7 s8 S min Phase 1: Find s min from a random sink Phase 2: Disseminate s min Phase 3: Build the perimeter from s min =s1 sink
Task 1: Perimeter Construction PA Message Complexity: N: Number of nodes in the network p: Number of nodes on the perimeter Phase 1: Identify s min O(N) messages. Phase 2: Disseminate s min O(N) messages Phase 3: Construct Perimeter O(p) messages Overall Message Complexity = O(N+p)
Task 2: Data Acquisition A) Data Acquisition takes place at the perimeter Perimeter Nodes sample at high frequencies Core Nodes are idle Energy Conservation B) Events are buffered in-situ on the perimeter s1 s2 s3 s4 s5 s6 s7 s8
Task 3: Data Replication Why Replication? Ensures that node failures will not subvert any detected events. Outline: Perimeter Nodes perform k-hop flooding of aggregated Events to neighbors. C=(3,4) B=(3,3) A=(2,2) Minimum Bounding Rectangle (MBR) E=(10,10) [(2,2), (10,10)] [(2,2), (4,5)] D=(4,5) F G
15 Task 3: Data Replication MBRs (Minimum Bounding Rectangles) aggregate the spatial coordinates. i.e., quadruple [X1,Y1,X2,Y2] However, sensor data is temporal! MBCs (Minimum Bounding Cuboids) aggregate both in space and in time. i.e., sextuple [X1,Y1,time1,X2,Y2,time2] TIME X Y
16 Task 4: Querying Post-process the MBRs or MBCs to answer historic queries, e.g., Query 1: Has the MSN identified any water? Solution: Yes if, Query 2: Where exactly? Solution: Combine the MBRs and the actual events (i.e., points) to derive the possible region.
17 Presentation Outline Motivation – Definitions The SenseSwarm Framework Task 1: Perimeter Construction Task 2: Data Acquisition Task 3: Data Replication Task 4: Query Execution Experimentation Conclusions & Future Work
18 Experimentation Dataset: synthetically derived from 54 sensors deployed at Intel Research Berkeley in 2004. Query: Conjunctive, historic Boolean queries of the type a&b&c&d Interesting Event Swarm Motion: We derive synthetic temporal coordinates using the Craig Reynolds algorithm (model of coordinated flock motion). Testbed: A custom simulator along with visualization modules. Energy Model: Crossbow’s TELOSB Sensor (250Kbps, RF On: 23mA) E=Vol x Amp x Sec Failure Rate: 20% of the nodes fail at random
19 Perimeter Construction Evaluation Perimeter Algorithm (PA) Vs. Centralized-PA (CPA) PA requires 85~89% less energy than CPA
20 Acquisition Cost Evaluation Uniform Scenario: All sensors participate SenseSwarm Scenario: Perimeter sensors participate, core nodes are idle. SenseSwarm: 75% less energy than Uniform PA periodic Execution
21 Presentation Outline Motivation – Definitions The SenseSwarm Framework Task 1: Perimeter Construction Task 2: Data Acquisition Task 3: Data Replication Task 4: Query Execution Experimentation Conclusions & Future Work
22 Conclusions We introduced SenseSwarm, a perimeter-based data acquisition framework for MSNs. We proposed: I.A new distributed perimeter algorithm; and II.A new in-network aggregation scheme. Future Work: I.Sink selection strategies II.Incremental perimeter update mechanisms III.Full Evaluation of Query Processing